def test_victor_purpura_algorithm_comparison(self):
assert_array_almost_equal(
stds.victor_purpura_dist([self.st21, self.st22, self.st23],
self.q3),
stds.victor_purpura_dist([self.st21, self.st22, self.st23],
self.q3,
algorithm='intuitive'))
def victor_purpura(simulObj, q):
vpd_spikes = [
neo.SpikeTrain(x,
units='milliseconds',
t_start=simulObj.t0,
t_stop=simulObj.tf) for x in simulObj.spikes
]
vpd_distance = spktd.victor_purpura_dist(vpd_spikes,
sort=False,
q=q * pq.Hz)
return vpd_distance
def test_wrong_input(self):
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.array1, self.array2], self.q3)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], self.q3)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], 5.0 * ms)
self.assertRaises(TypeError,
stds.victor_purpura_dist, [self.array1, self.array2],
self.q3,
algorithm='intuitive')
self.assertRaises(TypeError,
stds.victor_purpura_dist,
[self.qarray1, self.qarray2],
self.q3,
algorithm='intuitive')
self.assertRaises(TypeError,
stds.victor_purpura_dist,
[self.qarray1, self.qarray2],
5.0 * ms,
algorithm='intuitive')
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.array1, self.array2], self.tau3)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.qarray1, self.qarray2], self.tau3)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.qarray1, self.qarray2], 5.0 * Hz)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], self.tau2)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], 5.0)
self.assertRaises(TypeError,
stds.victor_purpura_dist, [self.st11, self.st13],
self.tau2,
algorithm='intuitive')
self.assertRaises(TypeError,
stds.victor_purpura_dist, [self.st11, self.st13],
5.0,
algorithm='intuitive')
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.st11, self.st13], self.q4)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.st11, self.st13], 5.0)
self.assertRaises(NotImplementedError,
stds.victor_purpura_dist, [self.st01, self.st02],
self.q3,
kernel=kernels.Kernel(2.0 / self.q3))
self.assertRaises(NotImplementedError,
stds.victor_purpura_dist, [self.st01, self.st02],
self.q3,
kernel=kernels.SymmetricKernel(2.0 / self.q3))
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st02],
self.q1,
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0,
1],
stds.victor_purpura_dist([self.st01, self.st02],
self.q3,
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0,
1])
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st02],
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0,
1],
1.0)
self.assertNotEqual(
stds.victor_purpura_dist([self.st01, self.st02],
kernel=kernels.AlphaKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0,
1],
1.0)
self.assertRaises(NameError,
stds.victor_purpura_dist, [self.st11, self.st13],
self.q2,
algorithm='slow')
def test_victor_purpura_distance_intuitive(self):
# Tests of distances of simplest spike trains
self.assertEqual(
stds.victor_purpura_dist([self.st00, self.st00],
self.q2,
algorithm='intuitive')[0, 1], 0.0)
self.assertEqual(
stds.victor_purpura_dist([self.st00, self.st01],
self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st00],
self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st01],
self.q2,
algorithm='intuitive')[0, 1], 0.0)
# Tests of distances under elementary spike operations
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st02],
self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st03],
self.q2,
algorithm='intuitive')[0, 1], 1.9)
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st04],
self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st05],
self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertEqual(
stds.victor_purpura_dist([self.st00, self.st07],
self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertAlmostEqual(
stds.victor_purpura_dist([self.st07, self.st08],
self.q4,
algorithm='intuitive')[0, 1], 0.4)
self.assertAlmostEqual(
stds.victor_purpura_dist([self.st07, self.st10],
self.q3,
algorithm='intuitive')[0, 1], 2.6)
self.assertEqual(
stds.victor_purpura_dist([self.st11, self.st14],
self.q2,
algorithm='intuitive')[0, 1], 1)
# Tests on timescales
self.assertEqual(
stds.victor_purpura_dist([self.st11, self.st14],
self.q1,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st11, self.st14],
self.q5,
algorithm='intuitive')[0, 1])
self.assertEqual(
stds.victor_purpura_dist([self.st07, self.st11],
self.q0,
algorithm='intuitive')[0, 1], 6.0)
self.assertEqual(
stds.victor_purpura_dist([self.st07, self.st11],
self.q1,
algorithm='intuitive')[0, 1], 6.0)
self.assertAlmostEqual(
stds.victor_purpura_dist([self.st07, self.st11],
self.q5,
algorithm='intuitive')[0, 1], 2.0, 5)
self.assertEqual(
stds.victor_purpura_dist([self.st07, self.st11],
self.q6,
algorithm='intuitive')[0, 1], 2.0)
# Tests on unordered spiketrains
self.assertEqual(
stds.victor_purpura_dist([self.st11, self.st13],
self.q4,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st12, self.st13],
self.q4,
algorithm='intuitive')[0, 1])
self.assertNotEqual(
stds.victor_purpura_dist([self.st11, self.st13],
self.q4,
sort=False,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st12, self.st13],
self.q4,
sort=False,
algorithm='intuitive')[0, 1])
# Tests on metric properties with random spiketrains
# (explicit calculation of second metric axiom in particular case,
# because from dist_matrix it is trivial)
dist_matrix = stds.victor_purpura_dist(
[self.st21, self.st22, self.st23], self.q3, algorithm='intuitive')
for i in range(3):
for j in range(3):
self.assertGreaterEqual(dist_matrix[i, j], 0)
if dist_matrix[i, j] == 0:
assert_array_equal(self.rd_st_list[i], self.rd_st_list[j])
assert_array_equal(
stds.victor_purpura_dist([self.st21, self.st22],
self.q3,
algorithm='intuitive'),
stds.victor_purpura_dist([self.st22, self.st21],
self.q3,
algorithm='intuitive'))
self.assertLessEqual(dist_matrix[0, 1],
dist_matrix[0, 2] + dist_matrix[1, 2])
self.assertLessEqual(dist_matrix[0, 2],
dist_matrix[1, 2] + dist_matrix[0, 1])
self.assertLessEqual(dist_matrix[1, 2],
dist_matrix[0, 1] + dist_matrix[0, 2])
# Tests on proper unit conversion
self.assertAlmostEqual(
stds.victor_purpura_dist([self.st14, self.st16],
self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st15, self.st16],
self.q3,
algorithm='intuitive')[0, 1])
self.assertAlmostEqual(
stds.victor_purpura_dist([self.st16, self.st14],
self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st16, self.st15],
self.q3,
algorithm='intuitive')[0, 1])
self.assertEqual(
stds.victor_purpura_dist([self.st01, self.st05],
self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st01, self.st05],
self.q7,
algorithm='intuitive')[0, 1])
# Tests on algorithmic behaviour for equal spike times
self.assertEqual(
stds.victor_purpura_dist([self.st31, self.st34],
self.q3,
algorithm='intuitive')[0, 1], 0.8 + 1.0)
self.assertEqual(
stds.victor_purpura_dist([self.st31, self.st34],
self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist([self.st32, self.st33],
self.q3,
algorithm='intuitive')[0, 1])
self.assertEqual(
stds.victor_purpura_dist(
[self.st31, self.st33], self.q3, algorithm='intuitive')[0, 1] *
2.0,
stds.victor_purpura_dist([self.st32, self.st34],
self.q3,
algorithm='intuitive')[0, 1])
# Tests on spike train list lengthes smaller than 2
self.assertEqual(
stds.victor_purpura_dist([self.st21],
self.q3,
algorithm='intuitive')[0, 0], 0)
self.assertEqual(
len(stds.victor_purpura_dist([], self.q3, algorithm='intuitive')),
0)
def test_wrong_input(self):
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.array1, self.array2], self.q3)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], self.q3)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], 5.0 * ms)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.array1, self.array2], self.q3,
algorithm='intuitive')
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], self.q3,
algorithm='intuitive')
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.qarray1, self.qarray2], 5.0 * ms,
algorithm='intuitive')
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.array1, self.array2], self.tau3)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.qarray1, self.qarray2], self.tau3)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.qarray1, self.qarray2], 5.0 * Hz)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], self.tau2)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], 5.0)
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], self.tau2,
algorithm='intuitive')
self.assertRaises(TypeError, stds.victor_purpura_dist,
[self.st11, self.st13], 5.0,
algorithm='intuitive')
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.st11, self.st13], self.q4)
self.assertRaises(TypeError, stds.van_rossum_dist,
[self.st11, self.st13], 5.0)
self.assertRaises(NotImplementedError, stds.victor_purpura_dist,
[self.st01, self.st02], self.q3,
kernel=kernels.Kernel(2.0 / self.q3))
self.assertRaises(NotImplementedError, stds.victor_purpura_dist,
[self.st01, self.st02], self.q3,
kernel=kernels.SymmetricKernel(2.0 / self.q3))
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st02], self.q1,
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0, 1],
stds.victor_purpura_dist(
[self.st01, self.st02], self.q3,
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0, 1])
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st02],
kernel=kernels.TriangularKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], 1.0)
self.assertNotEqual(stds.victor_purpura_dist(
[self.st01, self.st02],
kernel=kernels.AlphaKernel(
2.0 / (np.sqrt(6.0) * self.q2)))[0, 1], 1.0)
self.assertRaises(NameError, stds.victor_purpura_dist,
[self.st11, self.st13], self.q2, algorithm='slow')
def test_victor_purpura_algorithm_comparison(self):
assert_array_almost_equal(
stds.victor_purpura_dist([self.st21, self.st22, self.st23],
self.q3),
stds.victor_purpura_dist([self.st21, self.st22, self.st23],
self.q3, algorithm='intuitive'))
def test_victor_purpura_distance_intuitive(self):
# Tests of distances of simplest spike trains
self.assertEqual(stds.victor_purpura_dist(
[self.st00, self.st00], self.q2,
algorithm='intuitive')[0, 1], 0.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st00, self.st01], self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st00], self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st01], self.q2,
algorithm='intuitive')[0, 1], 0.0)
# Tests of distances under elementary spike operations
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st02], self.q2,
algorithm='intuitive')[0, 1], 1.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st03], self.q2,
algorithm='intuitive')[0, 1], 1.9)
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st04], self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st05], self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st00, self.st07], self.q2,
algorithm='intuitive')[0, 1], 2.0)
self.assertAlmostEqual(stds.victor_purpura_dist(
[self.st07, self.st08], self.q4,
algorithm='intuitive')[0, 1], 0.4)
self.assertAlmostEqual(stds.victor_purpura_dist(
[self.st07, self.st10], self.q3,
algorithm='intuitive')[0, 1], 2.6)
self.assertEqual(stds.victor_purpura_dist(
[self.st11, self.st14], self.q2,
algorithm='intuitive')[0, 1], 1)
# Tests on timescales
self.assertEqual(stds.victor_purpura_dist(
[self.st11, self.st14], self.q1,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st11, self.st14], self.q5,
algorithm='intuitive')[0, 1])
self.assertEqual(stds.victor_purpura_dist(
[self.st07, self.st11], self.q0,
algorithm='intuitive')[0, 1], 6.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st07, self.st11], self.q1,
algorithm='intuitive')[0, 1], 6.0)
self.assertAlmostEqual(stds.victor_purpura_dist(
[self.st07, self.st11], self.q5,
algorithm='intuitive')[0, 1], 2.0, 5)
self.assertEqual(stds.victor_purpura_dist(
[self.st07, self.st11], self.q6,
algorithm='intuitive')[0, 1], 2.0)
# Tests on unordered spiketrains
self.assertEqual(stds.victor_purpura_dist(
[self.st11, self.st13], self.q4,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st12, self.st13], self.q4,
algorithm='intuitive')[0, 1])
self.assertNotEqual(stds.victor_purpura_dist(
[self.st11, self.st13], self.q4,
sort=False, algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st12, self.st13], self.q4,
sort=False, algorithm='intuitive')[0, 1])
# Tests on metric properties with random spiketrains
# (explicit calculation of second metric axiom in particular case,
# because from dist_matrix it is trivial)
dist_matrix = stds.victor_purpura_dist(
[self.st21, self.st22, self.st23],
self.q3, algorithm='intuitive')
for i in range(3):
for j in range(3):
self.assertGreaterEqual(dist_matrix[i, j], 0)
if dist_matrix[i, j] == 0:
assert_array_equal(self.rd_st_list[i], self.rd_st_list[j])
assert_array_equal(stds.victor_purpura_dist(
[self.st21, self.st22], self.q3,
algorithm='intuitive'),
stds.victor_purpura_dist(
[self.st22, self.st21], self.q3,
algorithm='intuitive'))
self.assertLessEqual(dist_matrix[0, 1],
dist_matrix[0, 2] + dist_matrix[1, 2])
self.assertLessEqual(dist_matrix[0, 2],
dist_matrix[1, 2] + dist_matrix[0, 1])
self.assertLessEqual(dist_matrix[1, 2],
dist_matrix[0, 1] + dist_matrix[0, 2])
# Tests on proper unit conversion
self.assertAlmostEqual(stds.victor_purpura_dist(
[self.st14, self.st16], self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st15, self.st16], self.q3,
algorithm='intuitive')[0, 1])
self.assertAlmostEqual(stds.victor_purpura_dist(
[self.st16, self.st14], self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st16, self.st15], self.q3,
algorithm='intuitive')[0, 1])
self.assertEqual(stds.victor_purpura_dist(
[self.st01, self.st05], self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st01, self.st05], self.q7,
algorithm='intuitive')[0, 1])
# Tests on algorithmic behaviour for equal spike times
self.assertEqual(stds.victor_purpura_dist(
[self.st31, self.st34], self.q3,
algorithm='intuitive')[0, 1],
0.8 + 1.0)
self.assertEqual(stds.victor_purpura_dist(
[self.st31, self.st34], self.q3,
algorithm='intuitive')[0, 1],
stds.victor_purpura_dist(
[self.st32, self.st33], self.q3,
algorithm='intuitive')[0, 1])
self.assertEqual(stds.victor_purpura_dist(
[self.st31, self.st33], self.q3,
algorithm='intuitive')[0, 1] * 2.0,
stds.victor_purpura_dist(
[self.st32, self.st34], self.q3,
algorithm='intuitive')[0, 1])
# Tests on spike train list lengthes smaller than 2
self.assertEqual(stds.victor_purpura_dist(
[self.st21], self.q3,
algorithm='intuitive')[0, 0], 0)
self.assertEqual(len(stds.victor_purpura_dist(
[], self.q3, algorithm='intuitive')), 0)